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WO2021077315A1 - Systèmes et procédés de conduite autonome - Google Patents

Systèmes et procédés de conduite autonome Download PDF

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Publication number
WO2021077315A1
WO2021077315A1 PCT/CN2019/112654 CN2019112654W WO2021077315A1 WO 2021077315 A1 WO2021077315 A1 WO 2021077315A1 CN 2019112654 W CN2019112654 W CN 2019112654W WO 2021077315 A1 WO2021077315 A1 WO 2021077315A1
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WO
WIPO (PCT)
Prior art keywords
point cloud
cloud data
vehicle
groups
map
Prior art date
Application number
PCT/CN2019/112654
Other languages
English (en)
Inventor
Muchenxuan Tong
Yun Jiang
Original Assignee
Beijing Voyager Technology Co., Ltd.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Beijing Voyager Technology Co., Ltd. filed Critical Beijing Voyager Technology Co., Ltd.
Priority to CN201980002064.6A priority Critical patent/CN112105956B/zh
Priority to PCT/CN2019/112654 priority patent/WO2021077315A1/fr
Publication of WO2021077315A1 publication Critical patent/WO2021077315A1/fr

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • G01S17/93—Lidar systems specially adapted for specific applications for anti-collision purposes
    • G01S17/931—Lidar systems specially adapted for specific applications for anti-collision purposes of land vehicles
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/10—Path keeping
    • B60W30/12—Lane keeping
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
    • B60W40/04—Traffic conditions
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W60/00—Drive control systems specially adapted for autonomous road vehicles
    • B60W60/001—Planning or execution of driving tasks
    • B60W60/0015—Planning or execution of driving tasks specially adapted for safety
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/02—Systems using the reflection of electromagnetic waves other than radio waves
    • G01S17/06—Systems determining position data of a target
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • G01S17/89—Lidar systems specially adapted for specific applications for mapping or imaging
    • G01S17/894—3D imaging with simultaneous measurement of time-of-flight at a 2D array of receiver pixels, e.g. time-of-flight cameras or flash lidar
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2420/00—Indexing codes relating to the type of sensors based on the principle of their operation
    • B60W2420/40—Photo, light or radio wave sensitive means, e.g. infrared sensors
    • B60W2420/408—Radar; Laser, e.g. lidar
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • G01S17/89—Lidar systems specially adapted for specific applications for mapping or imaging

Definitions

  • the present disclosure generally relates to systems and methods for autonomous driving, and in particular, to systems and methods for processing point cloud data based on map information.
  • an autonomous driving system can quickly identify surrounding objects (e.g., other vehicles, obstacles) by processing the point cloud data and determine a driving path for a vehicle based on information (e.g., velocity information, position information) associated with the surrounding objects.
  • information e.g., velocity information, position information
  • the point cloud data associated with different objects e.g., a vehicle on a road and an obstacle which is located in the vicinity of the vehicle and outside the road
  • An aspect of the present disclosure relates to a system for autonomous driving.
  • the system may include a storage medium to store a set of instructions and a processor communicatively coupled to the storage medium.
  • the system may receive point cloud data captured by a sensor device; divide the point cloud data into a plurality of groups of point cloud data based on a map associated with the point cloud data; and process the plurality of groups of point cloud data.
  • the sensor device may include a light detection and ranging (Lidar) device.
  • Lidar light detection and ranging
  • the system may further project the point cloud data onto a two-dimensional plane corresponding to the map; identify at least one reference line on the two-dimensional plane based on the map; and divide the projected point cloud data into the plurality of groups of point cloud data based on the at least one reference line on the two-dimensional plane.
  • the at least one reference line may include a road boundary, a lane line, and/or a sidewalk.
  • the system may further perform an aggregation operation on the group of point cloud data and identify at least one object from the group of point cloud data based on the aggregation operation.
  • the system may further perform the aggregation operation on the group of point cloud data based on a plurality of point cloud blocks corresponding to a plurality of grids.
  • the at least one object may include a vehicle, a pedestrian, a building, and/or an obstacle.
  • system may further determine a driving path for the vehicle based on the at least one object and transmit signals to one or more control components of the vehicle to direct the vehicle to follow the driving path.
  • the map may contain information of an accuracy of a centimeter level and/or a millimeter level.
  • the point cloud data may be captured by the sensor device within a predetermined range from a vehicle.
  • Another aspect of the present disclosure relates to a method implemented on a computing device including at least one processor, at least one storage medium, and a communication platform connected to a network.
  • the method may include receiving point cloud data captured by a sensor device; dividing the point cloud data into a plurality of groups of point cloud data based on a map associated with the point cloud data; and processing the plurality of groups of point cloud data.
  • the sensor device may include a light detection and ranging (Lidar) device.
  • Lidar light detection and ranging
  • the dividing the point cloud data into the plurality of groups of point cloud data based on the map associated with the point cloud data may include projecting the point cloud data onto a two-dimensional plane corresponding to the map; identifying at least one reference line on the two-dimensional plane based on the map; and dividing the projected point cloud data into the plurality of groups of point cloud data based on the at least one reference line on the two-dimensional plane.
  • the at least one reference line may include a road boundary, a lane line, and/or a sidewalk.
  • the processing the plurality of groups of point cloud data may include for each of the plurality of groups of point cloud data, performing an aggregation operation on the group of point cloud data and identifying at least one object from the group of point cloud data based on the aggregation operation.
  • the performing the aggregation operation on the group of point cloud data may include performing the aggregation operation on the group of point cloud data based on a plurality of point cloud blocks corresponding to a plurality of grids.
  • the at least one object may include a vehicle, a pedestrian, a building, and/or an obstacle.
  • the method may further include determining a driving path for the vehicle based on the at least one object and transmitting signals to one or more control components of the vehicle to direct the vehicle to follow the driving path.
  • the map may contain information of an accuracy of a centimeter level and/or a millimeter level.
  • the point cloud data may be captured by the sensor device within a predetermined range from a vehicle.
  • a further aspect of the present disclosure relates to a vehicle configured for autonomous driving.
  • the vehicle may include a detecting component, a planning component, and a control component.
  • the planning component may be configured to receive point cloud data captured by the detecting component; divide the point cloud data into a plurality of groups of point cloud data based on a map associated with the point cloud data; and process the plurality of groups of point cloud data.
  • the detecting component may include a light detection and ranging (Lidar) device.
  • Lidar light detection and ranging
  • the planning component may be configured to project the point cloud data onto a two-dimensional plane corresponding to the map; identify at least one reference line on the two-dimensional plane based on the map; and divide the projected point cloud data into the plurality of groups of point cloud data based on the at least one reference line on the two-dimensional plane.
  • the at least one reference line may include a road boundary, a lane line, and/or a sidewalk.
  • the planning component may be configured to perform an aggregation operation on the group of point cloud data and identify at least one object from the group of point cloud data based on the aggregation operation.
  • the planning component may be configured to perform the aggregation operation on the group of point cloud data based on a plurality of point cloud blocks corresponding to a plurality of grids.
  • the at least one object may include a vehicle, a pedestrian, a building, and/or an obstacle.
  • the planning component may be further configured to determine a driving path for the vehicle based on the at least one object and transmit signals to one or more control components of the vehicle to direct the vehicle to follow the driving path.
  • the map may contain information of an accuracy of a centimeter level and/or a millimeter level.
  • the point cloud data may be captured by the detecting component within a predetermined range from a vehicle.
  • a still further aspect of the present disclosure relates to a system for autonomous driving.
  • the system may include a receiving module, a division module, and a processing module.
  • the receiving module may be configured to receive point cloud data captured by a sensor device.
  • the division module may be configured to divide the point cloud data into a plurality of groups of point cloud data based on a map associated with the point cloud data.
  • the processing module may be configured to process the plurality of groups of point cloud data.
  • a still further aspect of the present disclosure relates to a non-transitory computer readable medium including executable instructions.
  • the executable instructions When the executable instructions are executed by at least one processor, the executable instructions may direct the at least one processor to perform a method.
  • the method may include receiving point cloud data captured by a sensor device; dividing the point cloud data into a plurality of groups of point cloud data based on a map associated with the point cloud data; and processing the plurality of groups of point cloud data.
  • FIG. 1 is a schematic diagram illustrating an exemplary autonomous driving system according to some embodiments of the present disclosure
  • FIG. 2 is a schematic diagram illustrating exemplary hardware and/or software components of an exemplary computing device according to some embodiments of the present disclosure
  • FIG. 3 is a schematic diagram illustrating exemplary hardware and/or software components of an exemplary mobile device according to some embodiments of the present disclosure
  • FIG. 4 is a block diagram illustrating an exemplary processing device according to some embodiments of the present disclosure.
  • FIG. 5 is a flowchart illustrating an exemplary process for processing point cloud data according to some embodiments of the present disclosure
  • FIG. 6 is a flowchart illustrating an exemplary process for dividing point cloud data into a plurality of groups of point cloud data according to some embodiments of the present disclosure
  • FIG. 7-A is a schematic diagram illustrating exemplary reference lines on a map according to some embodiments of the present disclosure.
  • FIG. 7-B is a schematic diagram illustrating an exemplary process for dividing point cloud data into a plurality of groups of point cloud data according to some embodiments of the present disclosure.
  • FIG. 8 is a schematic diagram illustrating an exemplary aggregation operation based on point cloud blocks according to some embodiments of the present disclosure.
  • the flowcharts used in the present disclosure illustrate operations that systems implement according to some embodiments of the present disclosure. It is to be expressly understood, the operations of the flowcharts may be implemented not in order. Conversely, the operations may be implemented in inverted order, or simultaneously. Moreover, one or more other operations may be added to the flowcharts. One or more operations may be removed from the flowcharts.
  • the systems and methods disclosed in the present disclosure are described primarily regarding a transportation system in land, it should be understood that this is only one exemplary embodiment.
  • the systems and methods of the present disclosure may be applied to any other kind of transportation system.
  • the systems and methods of the present disclosure may be applied to transportation systems of different environments including land, ocean, aerospace, or the like, or any combination thereof.
  • the autonomous vehicle of the transportation systems may include a taxi, a private car, a hitch, a bus, a train, a bullet train, a high-speed rail, a subway, a vessel, an aircraft, a spaceship, a hot-air balloon, or the like, or any combination thereof.
  • the system may receive point cloud data captured by a sensor device (e.g., a Lidar) of a vehicle (e.g., an autonomous vehicle) and determine a map (e.g., a high-definition map) associated with the point cloud data.
  • a map e.g., a high-definition map
  • the system may divide the point cloud data into a plurality of groups of point cloud data. For example, the system may divide the point cloud data into a group of point cloud data on a road and a group of point cloud data outside the road based on a road boundary in the map. Further, the system may process the plurality of groups of point cloud data.
  • the system may perform an aggregation operation on each of the plurality of groups of point cloud data and identify at least one object (e.g., a vehicle, a pedestrian, a building, an obstacle) from the group of point cloud data based on the aggregation operation. Also, the system may determine a driving path for the vehicle based on the at least one identified object and direct the vehicle to follow the driving path.
  • point cloud data may be divided into a plurality of groups of point cloud data based on map information (e.g., a reference line in a map) , thereby improving the efficiency and accuracy of the processing of the point cloud data.
  • FIG. 1 is a schematic diagram illustrating an exemplary autonomous driving system according to some embodiments of the present disclosure.
  • the autonomous driving system 100 may include a vehicle 110 (e.g. 110-1, 110-2, ..., 110-n) , a server 120, a terminal device 130, a storage device 140, a network 150, and a positioning and navigation system 160.
  • the vehicle 110 may be any type of autonomous vehicle, unmanned aerial vehicle, etc.
  • an autonomous vehicle or an unmanned aerial vehicle may refer to a vehicle that is capable of achieving a certain level of driving automation.
  • Exemplary levels of driving automation may include a first level at which the vehicle is mainly supervised by a human and has a specific autonomous function (e.g., autonomous steering or accelerating) , a second level at which the vehicle has one or more advanced driver assistance systems (ADAS) (e.g., an adaptive cruise control system, a lane-keep system) that can control a braking, a steering, and/or an acceleration of the vehicle, a third level at which the vehicle is able to drive autonomously when one or more certain conditions are met, a fourth level at which the vehicle can operate without a human input or oversight but still is subject to some constraints (e.g., be confined to a certain area) , a fifth level at which the vehicle can operate autonomously under all circumstances, or the like, or any combination thereof.
  • ADAS advanced
  • the vehicle 110 may have equivalent structures that enable the vehicle 110 to move around or fly.
  • the vehicle 110 may include structures of a conventional vehicle, for example, a chassis, a suspension, a steering device (e.g., a steering wheel) , a brake device (e.g., a brake pedal) , an accelerator, etc.
  • the vehicle 110 may have a body and at least one wheel.
  • the body may be a body of any style, such as a sports vehicle, a coupe, a sedan, a pick-up truck, a station wagon, a sports utility vehicle (SUV) , a minivan, or a conversion van.
  • SUV sports utility vehicle
  • the at least one wheel may be configured as all-wheel drive (AWD) , front-wheel drive (FWR) , rear-wheel drive (RWD) , etc.
  • the vehicle 110 may be an electric vehicle, a fuel cell vehicle, a hybrid vehicle, a conventional internal combustion engine vehicle, etc.
  • the vehicle 110 may be capable of sensing its environment and navigating with one or more detecting units 112.
  • the plurality of detection units 112 may include a sensor device (e.g., a radar (e.g., a light detection and ranging (Lidar) device) ) , a global position system (GPS) module, an inertial measurement unit (IMU) , a camera, or the like, or any combination thereof.
  • the radar e.g., the Lidar device
  • the radar may be configured to scan the surrounding of the vehicle 110 and generate point cloud data.
  • the point cloud data may be used to generate digital three dimensional (3D) representations of one or more objects surrounding the vehicle 110.
  • the GPS module may refer to a device that is capable of receiving geolocation and time information from GPS satellites and then determining the device’s geographical position.
  • the IMU may refer to an electronic device that measures and provides a vehicle’s specific force, angular rate, and sometimes a magnetic field surrounding the vehicle, using various inertial sensors.
  • the various inertial sensors may include an acceleration sensor (e.g., a piezoelectric sensor) , a velocity sensor (e.g., a Hall sensor) , a distance sensor (e.g., a radar, an infrared sensor) , a steering angle sensor (e.g., a tilt sensor) , a traction-related sensor (e.g., a force sensor) , etc.
  • the camera may be configured to obtain one or more images relating to objects (e.g., a person, an animal, a tree, a roadblock, a building, or a vehicle) that are within the scope of the camera.
  • the server 120 may be a single server or a server group.
  • the server group may be centralized or distributed (e.g., the server 120 may be a distributed system) .
  • the server 120 may be local or remote.
  • the server 120 may access information and/or data stored in the terminal device 130, the detecting units 112, the vehicle 110, the storage device 140, and/or the positioning and navigation system 160 via the network 150.
  • the server 120 may be directly connected to the terminal device 130, the detecting units 112, the vehicle 110, and/or the storage device 140 to access stored information and/or data.
  • the server 120 may be implemented on a cloud platform or an onboard computer.
  • the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an inter-cloud, a multi-cloud, or the like, or any combination thereof.
  • the server 120 may be implemented on a computing device 200 including one or more components illustrated in FIG. 2 in the present disclosure.
  • the server 120 may include a processing device 122.
  • the processing device 122 may process information and/or data associated with driving information associated with the vehicle 110 to perform one or more functions described in the present disclosure. For example, the processing device 122 may divide point cloud data captured by the detecting units 112 (e.g., a sensor device) of the vehicle 110 into a plurality of groups of point cloud data and further process the plurality of groups of point cloud data respectively. Further, the processing device 122 may identify one or more objects based on the point cloud data and determine a driving path for the vehicle 110. That is, the processing device 122 may be configured as a planning component of the vehicle 110.
  • the processing device 122 may include one or more processing devices (e.g., single-core processing device (s) or multi-core processor (s) ) .
  • the processing device 122 may include a central processing unit (CPU) , an application-specific integrated circuit (ASIC) , an application-specific instruction-set processor (ASIP) , a graphics processing unit (GPU) , a physics processing unit (PPU) , a digital signal processor (DSP) , a field-programmable gate array (FPGA) , a programmable logic device (PLD) , a controller, a microcontroller unit, a reduced instruction set computer (RISC) , a microprocessor, or the like, or any combination thereof.
  • the processing device 122 may be integrated into the vehicle 110 and/or the terminal device 130.
  • the terminal device 130 may include a mobile device 130-1, a tablet computer 130-2, a laptop computer 130-3, a built-in device in a vehicle 130-4, a wearable device130-5, or the like, or any combination thereof.
  • the mobile device 130-1 may include a smart home device, a smart mobile device, a virtual reality device, an augmented reality device, or the like, or any combination thereof.
  • the smart home device may include a smart lighting device, a control device of an intelligent electrical apparatus, a smart monitoring device, a smart television, a smart video camera, an interphone, or the like, or any combination thereof.
  • the smart mobile device may include a smartphone, a personal digital assistant (PDA) , a gaming device, a navigation device, a point of sale (POS) device, or the like, or any combination thereof.
  • the virtual reality device and/or the augmented reality device may include a virtual reality helmet, a virtual reality glass, a virtual reality patch, an augmented reality helmet, an augmented reality glass, an augmented reality patch, or the like, or any combination thereof.
  • the virtual reality device and/or the augmented reality device may include a Google TM Glass, an Oculus Rift, a HoloLens, a Gear VR, etc.
  • the built-in device in the vehicle 130-4 may include an onboard computer, an onboard television, etc.
  • the wearable device 130-5 may include a smart bracelet, a smart footgear, a smart glass, a smart helmet, a smart watch, smart clothing, a smart backpack, a smart accessory, or the like, or any combination thereof.
  • the terminal device 130 may be a device with positioning technology for locating the location of the terminal device 130.
  • the server 120 may be integrated into the vehicle 110 and/or the terminal device 130.
  • the storage device 140 may store data and/or instructions.
  • the storage device 140 may store data obtained from the vehicle 110, the detecting units 112, the processing device 122, the terminal device 130, the positioning and navigation system 160, and/or an external storage device.
  • the storage device 140 may store the point cloud data captured by the detecting units 112.
  • the storage device 140 may store data and/or instructions that the server 120 may execute or use to perform exemplary methods described in the present disclosure.
  • the storage device 140 may store instructions that the processing device 122 may execute or use to divide the point cloud data into a plurality of groups of point cloud data.
  • the storage device 140 may include a mass storage, a removable storage, a volatile read-and-write memory, a read-only memory (ROM) , or the like, or any combination thereof.
  • Exemplary mass storage may include a magnetic disk, an optical disk, a solid-state drive, etc.
  • Exemplary removable storage may include a flash drive, a floppy disk, an optical disk, a memory card, a zip disk, a magnetic tape, etc.
  • Exemplary volatile read-and-write memory may include a random access memory (RAM) .
  • Exemplary RAM may include a dynamic RAM (DRAM) , a double date rate synchronous dynamic RAM (DDR SDRAM) , a static RAM (SRAM) , a thyristor RAM (T-RAM) , and a zero-capacitor RAM (Z-RAM) , etc.
  • Exemplary ROM may include a mask ROM (MROM) , a programmable ROM (PROM) , an erasable programmable ROM (EPROM) , an electrically-erasable programmable ROM (EEPROM) , a compact disk ROM (CD-ROM) , and a digital versatile disk ROM, etc.
  • the storage device 140 may be implemented on a cloud platform.
  • the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an inter-cloud, a multi-cloud, or the like, or any combination thereof.
  • the storage device 140 may be connected to the network 150 to communicate with one or more components (e.g., the server 120, the terminal device 130, the detecting units 112, the vehicle 110, and/or the positioning and navigation system 160) of the autonomous driving system 100.
  • One or more components of the autonomous driving system 100 may access the data or instructions stored in the storage device 140 via the network 150.
  • the storage device 140 may be directly connected to or communicate with one or more components (e.g., the server 120, the terminal device 130, the detecting units 112, the vehicle 110, and/or the positioning and navigation system 160) of the autonomous driving system 100.
  • the storage device 140 may be part of the server 120.
  • the storage device 140 may be integrated into the vehicle 110.
  • the network 150 may facilitate exchange of information and/or data.
  • one or more components e.g., the server 120, the terminal device 130, the detecting units 112, the vehicle 110, the storage device 140, or the positioning and navigation system 160
  • the server 120 may obtain a map associated with point cloud data from the storage device 140 via the network 150.
  • the network 150 may be any type of wired or wireless network, or combination thereof.
  • the network 150 may include a cable network, a wireline network, an optical fiber network, a telecommunications network, an intranet, an Internet, a local area network (LAN) , a wide area network (WAN) , a wireless local area network (WLAN) , a metropolitan area network (MAN) , a wide area network (WAN) , a public telephone switched network (PSTN) , a Bluetooth network, a ZigBee network, a near field communication (NFC) network, or the like, or any combination thereof.
  • the network 150 may include one or more network access points.
  • the network 150 may include wired or wireless network access points (e.g., 150-1, 150-2) , through which one or more components of the autonomous driving system 100 may be connected to the network 150 to exchange data and/or information.
  • the positioning and navigation system 160 may determine information associated with an object, for example, the terminal device 130, the vehicle 110, etc.
  • the positioning and navigation system 160 may include a global positioning system (GPS) , a global navigation satellite system (GLONASS) , a compass navigation system (COMPASS) , a BeiDou navigation satellite system, a Galileo positioning system, a quasi-zenith satellite system (QZSS) , etc.
  • the information may include a location, an elevation, a velocity, an acceleration of the object, a current time, etc.
  • the positioning and navigation system 160 may include one or more satellites, for example, a satellite 160-1, a satellite 160-2, and a satellite 160-3. The satellites 160-1 through 160-3 may determine the information mentioned above independently or jointly.
  • the positioning and navigation system 160 may send the information mentioned above to the server 120, the vehicle 110, and/or the terminal device 130 via wireless connections.
  • an element or component of the autonomous driving system 100 performs, the element may perform through electrical signals and/or electromagnetic signals.
  • a processor of the terminal device 130 may generate an electrical signal encoding the request.
  • the processor of the terminal device 130 may then transmit the electrical signal to an output port.
  • the output port may be physically connected to a cable, which further may transmit the electrical signal to an input port of the server 120.
  • the output port of the terminal device 130 may be one or more antennas, which convert the electrical signal to an electromagnetic signal.
  • an electronic device such as the terminal device 130 and/or the server 120
  • the processor retrieves or saves data from a storage medium (e.g., the storage device 140)
  • it may transmit out electrical signals to a read/write device of the storage medium, which may read or write structured data in the storage medium.
  • the structured data may be transmitted to the processor in the form of electrical signals via a bus of the electronic device.
  • an electrical signal may refer to one electrical signal, a series of electrical signals, and/or a plurality of discrete electrical signals.
  • FIG. 2 is a schematic diagram illustrating exemplary hardware and/or software components of an exemplary computing device according to some embodiments of the present disclosure.
  • the server 120 and/or the terminal device 130 may be implemented on the computing device 200.
  • the processing device 122 may be implemented on the computing device 200 and configured to perform functions of the processing device 122 disclosed in this disclosure.
  • the computing device 200 may be used to implement any component of the autonomous driving system 100 of the present disclosure.
  • the processing device 122 of the autonomous driving system 100 may be implemented on the computing device 200, via its hardware, software program, firmware, or a combination thereof.
  • the computer functions related to the autonomous driving system 100 as described herein may be implemented in a distributed manner on a number of similar platforms to distribute the processing load.
  • the computing device 200 may include communication (COM) ports 250 connected to and from a network (e.g., the network 150) connected thereto to facilitate data communications.
  • the computing device 200 may also include a processor (e.g., a processor 220) , in the form of one or more processors (e.g., logic circuits) , for executing program instructions.
  • the processor may include interface circuits and processing circuits therein.
  • the interface circuits may be configured to receive electronic signals from a bus 210, wherein the electronic signals encode structured data and/or instructions for the processing circuits to process.
  • the processing circuits may conduct logic calculations, and then determine a conclusion, a result, and/or an instruction encoded as electronic signals. Then the interface circuits may send out the electronic signals from the processing circuits via the bus 210.
  • the computing device 200 may further include program storage and data storage of different forms, for example, a disk 270, a read-only memory (ROM) 230, or a random access memory (RAM) 240, for storing various data files to be processed and/or transmitted by the computing device 200.
  • the computing device 200 may also include program instructions stored in the ROM 230, the RAM 240, and/or another type of non-transitory storage medium to be executed by the processor 220.
  • the methods and/or processes of the present disclosure may be implemented as the program instructions.
  • the computing device 200 also includes an I/O component 260, supporting input/output between the computing device 200 and other components therein.
  • the computing device 200 may also receive programming and data via network communications.
  • the computing device 200 in the present disclosure may also include multiple processors, and thus operations that are performed by one processor as described in the present disclosure may also be jointly or separately performed by the multiple processors.
  • the processor of the computing device 200 executes both operation A and operation B.
  • operation A and operation B may also be performed by two different processors jointly or separately in the computing device 200 (e.g., the first processor executes operation A and the second processor executes operation B, or the first and second processors jointly execute operations A and B) .
  • FIG. 3 is a schematic diagram illustrating exemplary hardware and/or software components of an exemplary mobile device according to some embodiments of the present disclosure.
  • the terminal device 130 may be implemented on the mobile device 300.
  • the mobile device 300 may include a communication platform 310, a display 320, a graphics processing unit (GPU) 330, a central processing unit (CPU) 340, an I/O 350, a memory 360, a mobile operating system (OS) 370, and storage 390.
  • any other suitable component including but not limited to a system bus or a controller (not shown) , may also be included in the mobile device 300.
  • the mobile operating system 370 e.g., iOS TM , Android TM , Windows Phone TM
  • the applications 380 may include a browser or any other suitable mobile app for receiving and rendering information relating to positioning or other information from the processing device 122.
  • User interactions with the information stream may be achieved via the I/O 350 and provided to the processing device 122 and/or other components of the autonomous driving system 100 via the network 150.
  • computer hardware platforms may be used as the hardware platform (s) for one or more of the elements described herein.
  • a computer with user interface elements may be used to implement a personal computer (PC) or any other type of work station or terminal device.
  • PC personal computer
  • a computer may also act as a server if appropriately programmed.
  • FIG. 4 is a block diagram illustrating an exemplary processing device according to some embodiments of the present disclosure.
  • the processing device 122 may include a receiving module 410, a division module 420, and a processing module 430.
  • the receiving module 410 may be configured to receive point cloud data captured by a sensor device (e.g., a Lidar device) associated with a subject (e.g., the vehicle 110) .
  • the receiving module 410 may receive the point cloud data from the sensor device, the storage device 140, and/or the terminal device 130 via the network 150. More descriptions of the point cloud data may be found elsewhere in the present disclosure (e.g., FIG. 5 and the descriptions thereof) .
  • the division module 420 may be configured to divide the point cloud data into a plurality of groups of point cloud data based on a map associated with the point cloud data.
  • the map may be a high-definition map containing information of an accuracy of a centimeter level or a millimeter level.
  • the division module 420 may divide the point cloud data into the plurality of groups of point cloud data based on at least one reference line on the map.
  • the at least one reference line may include a road boundary, a lane line, a line (e.g., a center line, an edge line) on a sidewalk, or the like, or a combination thereof. More descriptions of the division of the point cloud data may be found elsewhere in the present disclosure (e.g., FIG. 6 and the descriptions thereof) .
  • the processing module 430 may be configured to process the plurality of groups of point cloud data. In some embodiments, for each of the plurality of groups of point cloud data, the processing module 430 may segment the group of point cloud data into a plurality of point cloud blocks. In some embodiments, for each of the plurality of groups of point cloud data, the processing module 430 may perform an aggregation operation on the group of point cloud data and determine one or more point cloud clusters based on the aggregation operation. When performing the aggregation operation on the group of point cloud data, the processing module 430 may use the point cloud blocks as processing units. Further, the processing module 430 may identify at least one object from the group of point cloud data based on the one or more point cloud clusters.
  • the at least one object may include a vehicle, a pedestrian, a building, an obstacle, or the like, or a combination thereof.
  • the processing module 430 may determine a driving path for the subject (e.g., the vehicle 110) based on the at least one object. More descriptions of the processing of the point cloud data may be found elsewhere in the present disclosure (e.g., FIG. 5 and the descriptions thereof) .
  • the modules in the processing device 122 may be connected to or communicate with each other via a wired connection or a wireless connection.
  • the wired connection may include a metal cable, an optical cable, a hybrid cable, or the like, or any combination thereof.
  • the wireless connection may include a Local Area Network (LAN) , a Wide Area Network (WAN) , a Bluetooth, a ZigBee, a Near Field Communication (NFC) , or the like, or any combination thereof.
  • LAN Local Area Network
  • WAN Wide Area Network
  • Bluetooth a ZigBee
  • NFC Near Field Communication
  • the processing device 122 may also include a transmission module (not shown) configured to transmit signals (e.g., electrical signals, electromagnetic signals) to one or more control components (e.g., a steering device, an accelerator, a braking device) of the subject (e.g., the vehicle 110) to direct the subject (e.g., the vehicle 110) to follow the driving path.
  • the processing module 430 may include a determination unit (not shown) configured to determine the driving path for the subject (e.g., the vehicle 110) based on information (e.g., velocity information, position information, acceleration information) associated with the at least one object.
  • the processing device 122 may include a storage module (not shown) used to store information and/or data (e.g., the point cloud data, the map associated with the point cloud data, the plurality of groups of point cloud data) associated with the autonomous driving.
  • a storage module used to store information and/or data (e.g., the point cloud data, the map associated with the point cloud data, the plurality of groups of point cloud data) associated with the autonomous driving.
  • FIG. 5 is a flowchart illustrating an exemplary process for processing point cloud data according to some embodiments of the present disclosure.
  • the process 500 may be implemented as a set of instructions (e.g., an application) stored in the storage ROM 230 or RAM 240.
  • the processor 220 and/or the modules in FIG. 4 may execute the set of instructions, and when executing the instructions, the processor 220 and/or the modules may be configured to perform the process 500.
  • the operations of the illustrated process presented below are intended to be illustrative. In some embodiments, the process 500 may be accomplished with one or more additional operations not described and/or without one or more of the operations herein discussed. Additionally, the order in which the operations of the process as illustrated in FIG. 5 and described below is not intended to be limiting.
  • the processing device 122 may receive point cloud data captured by a sensor device (e.g., a Lidar device) associated with a subject (e.g., the vehicle 110) .
  • the processing device 122 may receive the point cloud data from the sensor device via the network 150.
  • the point cloud data may include a set of data points associated with one or more objects within a predetermined range of the subject (e.g., the vehicle 110) .
  • the predetermined range may be a default setting of the autonomous driving system 100 or may be adjustable under different situations.
  • the predetermined range may depend on (or partially depend on) a scanning range of the sensor device (e.g., the Lidar device) .
  • the one or more objects may include a vehicle, a pedestrian, a building, an obstacle, or the like, or any combination thereof.
  • the data point of the point cloud data may correspond to a physical point or region of an object in a space around an estimated location of the subject.
  • the sensor device may emit laser pulses to scan a surrounding environment of the subject.
  • the laser pulses may be reflected by physical points in the surrounding environment and return to the sensor device.
  • the sensor device may generate the point cloud data representative of the surrounding environment based on one or more characteristics of the return laser pulses.
  • the point cloud data may be captured according to a time period (e.g., 10 milliseconds, 100 milliseconds, 1 second, 2 seconds) when the subject (e.g., the vehicle 110) stops or travels along a road.
  • the sensor device may rotate in a scanning angle range (e.g., 360 degrees, 180 degrees, 120 degrees) and scan the surrounding environment in a certain scanning frequency (e.g., 10Hz, 15Hz, 20 Hz) .
  • a scanning angle range e.g., 360 degrees, 180 degrees, 120 degrees
  • a certain scanning frequency e.g. 10Hz, 15Hz, 20 Hz
  • the point cloud data may include at least one feature value of at least one feature of the physical points.
  • Exemplary features of a physical point may include a location (e.g., a geographic position, a relative position with respect to the sensor device) of the physical point, an intensity (e.g., a return strength of the laser pulses emitted from the sensor device and reflected by the physical point) of the physical point, a classification (e.g., a type) of the physical point, a scan direction (e.g., a direction in which a scanning mirror of the sensor device was directed to when a corresponding data point was detected) associated with the physical point, or the like, or any combination thereof.
  • the processing device 122 e.g., the division module 420
  • the processing circuits of the processor 220 may divide the point cloud data into a plurality of groups of point cloud data based on a map associated with the point cloud data.
  • the map may be a map presenting driving assistance information associated with a geographic region, such as a representation of a road network, for example, roads, intersections, traffic signals, lane rules, etc.
  • a range of the geographic region may be the same as or larger than the predetermined range where the point cloud data is captured.
  • a shape of the geographic region may be a regular triangle, a rectangle, a square, a regular hexagon, a circle, etc.
  • the shape of the geographic region may be a rectangle with a size of M meters ⁇ N meters, wherein M and N may be positive numbers (e.g., 5, 10, 20, 50, 100, 500) .
  • the map may be a three-dimensional (3D) map, a two-dimensional (2D) map, a four-dimensional (4D) map, etc.
  • the map may be a high-definition map containing information of an accuracy of a centimeter level or a millimeter level.
  • the high-definition map may be generated online or offline.
  • the high-definition map may be generated offline based on data (e.g., point cloud data) captured by a plurality of detection units (e.g., the detection units described in FIG. 1) installed on a test vehicle which is used to execute a measurement trip. As the test vehicle moves along a road, the plurality of detection units may generate point cloud data associated with a surrounding environment of the test vehicle.
  • a processing device may generate a plurality of high-definition maps corresponding to different geographic regions based on the point cloud data and store the plurality of high-definition maps in a storage device (e.g., the storage device 140) of the autonomous driving system 100. Accordingly, the processing device 122 may access the storage device and retrieve a corresponding high-definition map based on an estimated location of the subject.
  • the point cloud data associated with different objects may be almost connected together, for example, when a vehicle is driving along an outermost edge of a road, point cloud data associated with the vehicle may be almost connected with point cloud data associated with a flower bed which is located outside the road and in the vicinity of the outermost edge of the road, which may result in that an error may occur in the processing of the point cloud data (e.g., in the identification of the objects) .
  • the processing device 122 may divide the point cloud data into a plurality of groups of point cloud data based on the map associated with point cloud data.
  • the processing device 122 may divide the point cloud data into a group of point cloud data associated with objects (e.g., a vehicle) on a road and a group of point cloud data associated with objects (e.g., a flower bed, a tree, a pole, a road railing, a building) outside the road based on a road boundary.
  • the processing device 122 may divide the point cloud data into a group of point cloud data associated with objects (e.g., a vehicle, an obstacle) on a road and a group of point cloud data associated with objects (e.g., a pedestrian) on a sidewalk based on a line (e.g., an edge line) on the sidewalk.
  • the processing device 122 may divide the point cloud data into a group of point cloud data associated with objects on a left lane of a road and a group of point cloud data associated with objects on a right lane of the road based on a lane line. More descriptions of the division of the point cloud data may be found elsewhere in the present disclosure (e.g., FIG. 6 and the descriptions thereof) .
  • the processing device 122 may process the plurality of groups of point cloud data.
  • the processing device 122 may segment the point cloud data into a plurality of point cloud blocks and process the plurality of groups of point cloud data based on the point cloud blocks.
  • the processing device 122 may specify a plurality of grids on a two-dimensional plane corresponding to the map and join the plurality of groups of point cloud data with the plurality of grids.
  • a shape of the plurality of grids may include a quadrangle, a hexagon, an irregular polygon, or the like, or a combination thereof.
  • a size of the plurality of grids may be default setting (e.g., 20 cm ⁇ 20 cm) of the autonomous driving system 100 or may be adjustable under different situations.
  • the processing device 122 may integrate data points (which may be expressed as multiple data records) within the grid as a point cloud block (which may be expressed as a single data record) . Accordingly, the large amount of data points of the point cloud data can be integrated as a plurality of point cloud blocks corresponding to the plurality of grids respectively, thereby speeding up subsequent data reading and/or data processing.
  • the processing device 122 may determine the plurality of point cloud blocks according to a geohash algorithm. For each of the data points of the point cloud data, the processing device 122 may obtain a longitude and a latitude of the data point and determine a character string (also referred to as a geohash value) based on the longitude and the latitude according to the geohash algorithm. For any two data points, the more similar the character strings corresponding to the two data points are, the smaller the distance between the two data points may be. Further, the processing device 122 may determine the plurality of point cloud blocks based on the character strings corresponding to the data points.
  • the processing device 122 may integrate data points within a region (e.g., a rectangle region, which is similar to the grid to a certain extent) as a point cloud block, which may be expressed as a same character string corresponding to the data points within the region. Similar to the above, the large amount of data points of the point cloud data can be integrated as a plurality of point cloud blocks corresponding to the plurality of regions respectively, thereby speeding up subsequent data reading and/or data processing.
  • a region e.g., a rectangle region, which is similar to the grid to a certain extent
  • a point cloud block which may be expressed as a same character string corresponding to the data points within the region.
  • the large amount of data points of the point cloud data can be integrated as a plurality of point cloud blocks corresponding to the plurality of regions respectively, thereby speeding up subsequent data reading and/or data processing.
  • the processing device 122 may perform an aggregation operation on the group of point cloud data and determine one or more point cloud clusters based on the aggregation operation.
  • the processing device 122 may use the point cloud blocks as processing units, which can improve processing speed and reduce processing time.
  • the processing device 122 may identify at least one object from the group of point cloud data based on the one or more point cloud clusters.
  • the at least one object may include a vehicle, a pedestrian, a building, an obstacle, or the like, or a combination thereof.
  • the processing device 122 may identify the at least one object based on an object identification method.
  • Exemplary object identification method may include an object recognition method based on local features, an object recognition method based on global features, an object recognition method based on a graph matching, an object recognition algorithm based on a machine learning, or the like, or a combination thereof.
  • the processing device 122 may determine a driving path for the subject (e.g., the vehicle 110) based on information (e.g., velocity information, position information, acceleration information) associated with the at least one object and transmit signals (e.g., electrical signals, electromagnetic signals) to one or more control components (e.g., a steering device, an accelerator, a braking device) of the subject (e.g., the vehicle 110) to direct the subject (e.g., the vehicle 110) to follow the driving path.
  • information e.g., velocity information, position information, acceleration information
  • signals e.g., electrical signals, electromagnetic signals
  • one or more other optional operations may be added elsewhere in the process 500.
  • the processing device 122 may store information and/or data (e.g., the point cloud data, the map associated with the point cloud data, the plurality of groups of point cloud data) associated with the autonomous driving in a storage device (e.g., the storage device 140) disclosed elsewhere in the present disclosure.
  • operation 510 and operation 520 may be combined into a single operation in which the processing device 122 may both receive the point cloud data captured by the sensor device and divide the point cloud data into the plurality of groups of point cloud data.
  • the processing device 112 may receive the point cloud data from the storage device 140 or the terminal device 130 via the network 150.
  • the processing device 122 may segment the point cloud data into the plurality of point cloud blocks (e.g., the point clout blocks corresponding to the plurality of grids) before dividing the point cloud into the plurality of groups of point cloud data.
  • FIG. 6 is a flowchart illustrating an exemplary process for dividing point cloud data into a plurality of groups of point cloud data according to some embodiments of the present disclosure.
  • the process 600 may be implemented as a set of instructions (e.g., an application) stored in the storage ROM 230 or RAM 240.
  • the processor 220 and/or the modules in FIG. 4 may execute the set of instructions, and when executing the instructions, the processor 220 and/or the modules may be configured to perform the process 600.
  • the operations of the illustrated process presented below are intended to be illustrative. In some embodiments, the process 600 may be accomplished with one or more additional operations not described and/or without one or more of the operations herein discussed. Additionally, the order in which the operations of the process as illustrated in FIG. 6 and described below is not intended to be limiting.
  • the processing device 122 e.g., the division module 420
  • the processing circuits of the processor 220 may project the point cloud data onto a two-dimensional plane corresponding to the map.
  • the map may be a three-dimensional map which can be represented in a three-dimensional rectangular coordinate system including an X-axis, a Y-axis, and a Z-axis.
  • the two-dimensional plane may be the X-Y plane corresponding to the geographic region of the three-dimensional map.
  • the processing device 122 may project the point cloud data onto the two-dimensional plane according to a projection method, for example, a central projection method, a parallel projection method, etc.
  • a projection method for example, a central projection method, a parallel projection method, etc.
  • the physical point corresponds to a projected point on the X-Y plane with a coordinate (x, y) , wherein x may refer to a longitude of the physical point and y may refer to a latitude of the physical point.
  • the processing device 122 may identify at least one reference line on the two-dimensional plane based on the map.
  • the at least one reference line may include a road boundary, a lane line, a line (e.g., a center line, an edge line) on a sidewalk, or the like, or a combination thereof.
  • the map may be a high-definition map, accordingly, the reference line may be a line with a high-definition (e.g., an accuracy of a centimeter level or a millimeter level) .
  • the reference line may be a straight line or a curve.
  • the processing device 122 may divide the projected point cloud data into a plurality of groups of point cloud data based on the at least one reference line on the two-dimensional plane.
  • the processing device 122 may divide the projected point cloud data into a group of point cloud data projected in a region on a road and a group of point cloud data projected in a region outside the road.
  • the processing device 122 may divide the projected point cloud data into a group of point cloud data projected in a region located in a left lane of the road and a group of point cloud data projected in a region located in a right lane of the road.
  • the processing device 122 may divide the projected point cloud data into a group of point cloud data projected in a region located in the sidewalk and a group of point cloud data projected in a region located outside the sidewalk. More descriptions of the division of the projected point cloud data may be found elsewhere in the present disclosure (e.g., FIGs. 7-A and 7-B and the descriptions thereof) .
  • the processing device 122 may project the plurality of groups of point cloud data back to the three-dimensional space for further processing (e.g., identifying objects based on the point cloud data) .
  • one or more other optional operations may be added elsewhere in the process 600.
  • the processing device 122 may store information and/or data (e.g., the two-dimensional plane, the at least one reference line) associated with the division of the projected point cloud data in a storage device (e.g., the storage device 140) disclosed elsewhere in the present disclosure.
  • operation 610 and operation 620 may be combined into a single operation in which the processing device 112 may both project the point cloud data onto the two-dimensional plane corresponding to the map and identify the at least one reference line on the two-dimensional plane.
  • FIG. 7-A is a schematic diagram illustrating exemplary reference lines on a map according to some embodiments of the present disclosure.
  • the detecting units 112 of the vehicle 110 may capture point cloud data associated with one or more objects (e.g., a vehicle 710, a pedestrian (e.g., 721, 722) , a building (e.g., a bus stop 731, a flower bed 732) , an obstacle (e.g., a stone 740) ) within a predetermined range from the vehicle 110.
  • objects e.g., a vehicle 710, a pedestrian (e.g., 721, 722) , a building (e.g., a bus stop 731, a flower bed 732) , an obstacle (e.g., a stone 740) ) within a predetermined range from the vehicle 110.
  • objects e.g., a vehicle 710, a pedestrian (e.g., 721, 722) , a building (e.g.,
  • the processing device 122 may obtain a high-definition map 700 (for brevity, a two-dimensional plane corresponding to the map is shown) from a storage device (e.g., the storage device 140 or an external data resource) , which presents driving assistance information associated with a geographic region the same as or larger than the predetermined range from the vehicle 110. Further, as described in connection with FIG. 6, the processing device 122 may divide the point cloud data into a plurality of groups of point cloud data based on at least one reference line on the map.
  • the at least one reference line may include a road boundary (e.g., a right boundary 751, a left boundary 752) , a lane line 760, a line (e.g., a left edge line 771, a lower edge line 772, a center line 773, an upper edge line 774, a right edge line 775) on a sidewalk, etc.
  • a road boundary e.g., a right boundary 751, a left boundary 752
  • a lane line 760 e.g., a left edge line 771, a lower edge line 772, a center line 773, an upper edge line 774, a right edge line 775
  • FIG. 7-B is a schematic diagram illustrating an exemplary process for dividing the point cloud data into a plurality of groups of point cloud data according to some embodiments of the present disclosure.
  • the processing device 122 may project the point cloud data associated with the one or more objects (e.g., the vehicle 710, the pedestrian (e.g., 721, 722) , the building (e.g., the bus stop 731, the flower bed 732) , the obstacle (e.g., the stone 740) illustrated in FIG. 7-A) onto the two-dimensional plane 700 and obtain the projected point cloud data (e.g., 710’, 721’, 722’, 731’, 732’, 740’) corresponding to the one or more objects.
  • the objects e.g., the vehicle 710, the pedestrian (e.g., 721, 722) , the building (e.g., the bus stop 731, the flower bed 732) , the obstacle (e.g., the stone 740) illustrated in FIG. 7
  • part of the projected point cloud data is partially connected together (e.g., 710’ and 732’, 721’ and 740’) , which may result in an error in the processing of the point cloud data, for example, the vehicle 710 and the flower bed 732 may not be accurately identified.
  • the processing device 122 divides the projected point cloud data into a plurality of groups of point cloud data, for example, a first group of point cloud data (e.g., point cloud data within a quadrangle formed by the four edge lines of the sidewalk) , a second group of point cloud data (e.g., point cloud data within a quadrangle formed by the right boundary 751, a right portion of the lower edge line 775 of the sidewalk, and the lane line 760) , a third group of point cloud data (e.g., point cloud data within a quadrangle formed by the left boundary 752, a left portion of the lower edge line 775 of the sidewalk, and the lane line 760) , a fourth group of point cloud data (e.g., point cloud data within a region outside the road) , etc.
  • a first group of point cloud data e.g., point cloud data within a quadrangle formed by the four edge lines of the sidewalk
  • a second group of point cloud data e.g., point cloud data
  • FIG. 8 is a schematic diagram illustrating an exemplary aggregation operation based on point cloud blocks according to some embodiments of the present disclosure.
  • the processing device 122 may determine a plurality of point cloud blocks corresponding to a plurality of grids on the two-dimensional plane corresponding to the map and perform an aggregation operation on each of the plurality of groups of the point cloud data based on the plurality of point cloud blocks. For brevity, only a portion of the plurality of groups of point cloud data is shown.
  • a shape of the plurality of grids may be a square.
  • the processing device 122 may join the point cloud data (which includes a plurality of data points) with the plurality of grids. Further, when performing the aggregation operation on the point cloud data, the processing device 122 may use the point cloud blocks as processing units. For example, for the group of point cloud data projected in a region on a road, the processing device 122 may access data records corresponding to point cloud blocks corresponding to grids represented by a solid box 802, perform the aggregation operation on the point cloud blocks, and identify a vehicle (e.g., the vehicle 710) based on the aggregation operation.
  • a vehicle e.g., the vehicle 710
  • the processing device 122 may access data records corresponding to point cloud blocks corresponding to grids represented by a dashed box 804, perform the aggregation operation on the point cloud blocks, and identify a flower bed (e.g., the flower bed 732) based on the aggregation operation.
  • a flower bed e.g., the flower bed 732
  • aspects of the present disclosure may be illustrated and described herein in any of a number of patentable classes or context including any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof. Accordingly, aspects of the present disclosure may be implemented entirely hardware, entirely software (including firmware, resident software, micro-code, etc. ) or combining software and hardware implementation that may all generally be referred to herein as a “unit, ” “module, ” or “system. ” Furthermore, aspects of the present disclosure may take the form of a computer program product embodied in one or more computer readable media having computer readable program code embodied thereon.
  • a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including electro-magnetic, optical, or the like, or any suitable combination thereof.
  • a computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that may communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
  • Program code embodied on a computer readable signal medium may be transmitted using any appropriate medium, including wireless, wireline, optical fiber cable, RF, or the like, or any suitable combination of the foregoing.
  • Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET, Python or the like, conventional procedural programming languages, such as the "C" programming language, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages such as Python, Ruby and Groovy, or other programming languages.
  • the program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server.
  • the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN) , or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider) or in a cloud computing environment or offered as a service such as a Software as a Service (SaaS) .
  • LAN local area network
  • WAN wide area network
  • SaaS Software as a Service

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Abstract

La présente invention concerne des systèmes et des procédés de conduite autonome. Le système peut recevoir des données de nuage de points capturées par un dispositif de capteur. Le système peut également diviser les données de nuage de points en une pluralité de groupes de données de nuage de points sur la base d'une carte associée aux données de nuage de points. Le système peut en outre traiter la pluralité de groupes de données de nuage de points.
PCT/CN2019/112654 2019-10-23 2019-10-23 Systèmes et procédés de conduite autonome WO2021077315A1 (fr)

Priority Applications (2)

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CN201980002064.6A CN112105956B (zh) 2019-10-23 2019-10-23 用于自动驾驶的系统和方法
PCT/CN2019/112654 WO2021077315A1 (fr) 2019-10-23 2019-10-23 Systèmes et procédés de conduite autonome

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PCT/CN2019/112654 WO2021077315A1 (fr) 2019-10-23 2019-10-23 Systèmes et procédés de conduite autonome

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